





import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor






def      lowerCamelCase__      (__lowerCamelCase						):
     return (data["data"], data["target"])






def      lowerCamelCase__      (__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase						):
     _SCREAMING_SNAKE_CASE							:						Any         =				XGBRegressor(verbosity=0,							random_state=42						)
     xgb.fit(SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__						)
     # Predict target for test data
     _SCREAMING_SNAKE_CASE							:						str         =				xgb.predict(SCREAMING_SNAKE_CASE__						)
     _SCREAMING_SNAKE_CASE							:						List[Any]         =				predictions.reshape(len(SCREAMING_SNAKE_CASE__						),							1						)
     return predictions






def      lowerCamelCase__      ():
     _SCREAMING_SNAKE_CASE							:						Tuple         =				fetch_california_housing()
     _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				data_handling(SCREAMING_SNAKE_CASE__						)
     _SCREAMING_SNAKE_CASE							:						str         =				train_test_split(
         SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							test_size=0.25,							random_state=1						)
     _SCREAMING_SNAKE_CASE							:						int         =				xgboost(SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__						)
     # Error printing
     print(f"""Mean Absolute Error : {mean_absolute_error(SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__						)}"""						)
     print(f"""Mean Square Error  : {mean_squared_error(SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__						)}"""						)


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)
       main()



'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_						:  str				  =						logging.get_logger(__name__)

UpperCAmelCase_						:  Optional[int]				  =						{
    'SCUT-DLVCLab/lilt-roberta-en-base': (
        'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
    ),
}





class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							A_				: List[str]       =    """lilt"""






							def __init__(       self       ,   __snake_case=3_0522       ,   __snake_case=768       ,   __snake_case=12       ,   __snake_case=12       ,   __snake_case=3072       ,   __snake_case="gelu"       ,   __snake_case=0.1       ,   __snake_case=0.1       ,   __snake_case=512       ,   __snake_case=2       ,   __snake_case=0.02       ,   __snake_case=1e-12       ,   __snake_case=0       ,   __snake_case="absolute"       ,   __snake_case=None       ,   __snake_case=4       ,   __snake_case=1024       ,   **__snake_case       ,   ):
											super().__init__(pad_token_id=__snake_case       ,   **__snake_case    )

											_SCREAMING_SNAKE_CASE					:			str							= vocab_size
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= hidden_size
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= num_hidden_layers
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= num_attention_heads
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= hidden_act
											_SCREAMING_SNAKE_CASE					:			Dict							= intermediate_size
											_SCREAMING_SNAKE_CASE					:			Tuple							= hidden_dropout_prob
											_SCREAMING_SNAKE_CASE					:			List[str]							= attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= max_position_embeddings
											_SCREAMING_SNAKE_CASE					:			Dict							= type_vocab_size
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= initializer_range
											_SCREAMING_SNAKE_CASE					:			Tuple							= layer_norm_eps
											_SCREAMING_SNAKE_CASE					:			str							= position_embedding_type
											_SCREAMING_SNAKE_CASE					:			List[str]							= classifier_dropout
											_SCREAMING_SNAKE_CASE					:			int							= channel_shrink_ratio
											_SCREAMING_SNAKE_CASE					:			str							= max_ad_position_embeddings




